Overview

Dataset statistics

Number of variables25
Number of observations45811
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 MiB
Average record size in memory200.0 B

Variable types

Numeric13
Categorical12

Warnings

ASSESSMENT_NBHD has a high cardinality: 55 distinct values High cardinality
Unnamed: 0 is highly correlated with ZIPCODE and 1 other fieldsHigh correlation
BATHRM is highly correlated with PRICEHigh correlation
PRICE is highly correlated with BATHRM and 1 other fieldsHigh correlation
FIREPLACES is highly correlated with PRICEHigh correlation
ZIPCODE is highly correlated with Unnamed: 0High correlation
LATITUDE is highly correlated with LONGITUDEHigh correlation
LONGITUDE is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Unnamed: 0 is highly correlated with PRICE and 2 other fieldsHigh correlation
BATHRM is highly correlated with PRICEHigh correlation
PRICE is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
ZIPCODE is highly correlated with Unnamed: 0High correlation
LATITUDE is highly correlated with LONGITUDEHigh correlation
LONGITUDE is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with ZIPCODEHigh correlation
ZIPCODE is highly correlated with Unnamed: 0High correlation
AC is highly correlated with EYB and 2 other fieldsHigh correlation
BATHRM is highly correlated with PRICEHigh correlation
GRADE is highly correlated with QUADRANT and 6 other fieldsHigh correlation
QUADRANT is highly correlated with GRADE and 6 other fieldsHigh correlation
YR_RMDL is highly correlated with EYB and 1 other fieldsHigh correlation
ASSESSMENT_NBHD is highly correlated with GRADE and 11 other fieldsHigh correlation
ZIPCODE is highly correlated with QUADRANT and 7 other fieldsHigh correlation
EYB is highly correlated with AC and 4 other fieldsHigh correlation
EXTWALL is highly correlated with ASSESSMENT_NBHDHigh correlation
HEAT is highly correlated with ACHigh correlation
LANDAREA is highly correlated with PRICEHigh correlation
LONGITUDE is highly correlated with GRADE and 6 other fieldsHigh correlation
PRICE is highly correlated with BATHRM and 2 other fieldsHigh correlation
WARD is highly correlated with GRADE and 8 other fieldsHigh correlation
KITCHENS is highly correlated with STRUCTHigh correlation
ROOF is highly correlated with ASSESSMENT_NBHD and 4 other fieldsHigh correlation
STRUCT is highly correlated with ASSESSMENT_NBHD and 4 other fieldsHigh correlation
STYLE is highly correlated with ASSESSMENT_NBHDHigh correlation
Unnamed: 0 is highly correlated with GRADE and 7 other fieldsHigh correlation
LATITUDE is highly correlated with QUADRANT and 5 other fieldsHigh correlation
CNDTN is highly correlated with AC and 2 other fieldsHigh correlation
AC is highly correlated with HEATHigh correlation
WARD is highly correlated with QUADRANT and 1 other fieldsHigh correlation
QUADRANT is highly correlated with WARD and 1 other fieldsHigh correlation
ASSESSMENT_NBHD is highly correlated with WARD and 1 other fieldsHigh correlation
HEAT is highly correlated with ACHigh correlation
Unnamed: 0 has unique values Unique
HF_BATHRM has 17530 (38.3%) zeros Zeros
YR_RMDL has 18129 (39.6%) zeros Zeros
FIREPLACES has 24362 (53.2%) zeros Zeros

Reproduction

Analysis started2021-07-10 09:23:08.351452
Analysis finished2021-07-10 09:23:48.049989
Duration39.7 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct45811
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49924.12436
Minimum0
Maximum106687
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:48.173592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4760.5
Q122999.5
median48103
Q376620.5
95-th percentile100330
Maximum106687
Range106687
Interquartile range (IQR)53621

Descriptive statistics

Standard deviation30672.10125
Coefficient of variation (CV)0.6143743459
Kurtosis-1.195823393
Mean49924.12436
Median Absolute Deviation (MAD)26650
Skewness0.1388722242
Sum2287074061
Variance940777795.1
MonotonicityStrictly increasing
2021-07-10T09:23:48.388380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
941681
 
< 0.1%
48231
 
< 0.1%
253051
 
< 0.1%
969861
 
< 0.1%
191641
 
< 0.1%
171171
 
< 0.1%
887981
 
< 0.1%
416971
 
< 0.1%
478421
 
< 0.1%
Other values (45801)45801
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
31
< 0.1%
51
< 0.1%
71
< 0.1%
81
< 0.1%
141
< 0.1%
161
< 0.1%
191
< 0.1%
221
< 0.1%
ValueCountFrequency (%)
1066871
< 0.1%
1066731
< 0.1%
1066721
< 0.1%
1066681
< 0.1%
1066661
< 0.1%
1066641
< 0.1%
1066631
< 0.1%
1066621
< 0.1%
1066571
< 0.1%
1066561
< 0.1%

BATHRM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.246862107
Minimum0
Maximum12
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:48.568014image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.048311669
Coefficient of variation (CV)0.4665669804
Kurtosis2.006920455
Mean2.246862107
Median Absolute Deviation (MAD)1
Skewness0.9360509198
Sum102931
Variance1.098957355
MonotonicityNot monotonic
2021-07-10T09:23:48.741652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
217218
37.6%
111920
26.0%
311690
25.5%
43910
 
8.5%
5715
 
1.6%
6248
 
0.5%
768
 
0.1%
821
 
< 0.1%
97
 
< 0.1%
05
 
< 0.1%
Other values (3)9
 
< 0.1%
ValueCountFrequency (%)
05
 
< 0.1%
111920
26.0%
217218
37.6%
311690
25.5%
43910
 
8.5%
5715
 
1.6%
6248
 
0.5%
768
 
0.1%
821
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
121
 
< 0.1%
113
 
< 0.1%
105
 
< 0.1%
97
 
< 0.1%
821
 
< 0.1%
768
 
0.1%
6248
 
0.5%
5715
 
1.6%
43910
 
8.5%
311690
25.5%

HF_BATHRM
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6840496824
Minimum0
Maximum7
Zeros17530
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:48.902633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5990047611
Coefficient of variation (CV)0.8756743502
Kurtosis0.6323083574
Mean0.6840496824
Median Absolute Deviation (MAD)0
Skewness0.4278787713
Sum31337
Variance0.3588067038
MonotonicityNot monotonic
2021-07-10T09:23:49.056242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
125388
55.4%
017530
38.3%
22767
 
6.0%
398
 
0.2%
421
 
< 0.1%
56
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
017530
38.3%
125388
55.4%
22767
 
6.0%
398
 
0.2%
421
 
< 0.1%
56
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
56
 
< 0.1%
421
 
< 0.1%
398
 
0.2%
22767
 
6.0%
125388
55.4%
017530
38.3%

HEAT
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Forced Air
18112 
Hot Water Rad
15086 
Warm Cool
11647 
Ht Pump
 
662
Water Base Brd
 
79
Other values (9)
 
225

Length

Max length14
Median length10
Mean length10.70369562
Min length7

Characters and Unicode

Total characters490347
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWarm Cool
2nd rowHot Water Rad
3rd rowHot Water Rad
4th rowHot Water Rad
5th rowHot Water Rad

Common Values

ValueCountFrequency (%)
Forced Air18112
39.5%
Hot Water Rad15086
32.9%
Warm Cool11647
25.4%
Ht Pump662
 
1.4%
Water Base Brd79
 
0.2%
Wall Furnace57
 
0.1%
Elec Base Brd52
 
0.1%
Electric Rad28
 
0.1%
Gravity Furnac27
 
0.1%
Air-Oil26
 
0.1%
Other values (4)35
 
0.1%

Length

2021-07-10T09:23:49.422856image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
air18121
17.0%
forced18112
17.0%
water15165
14.2%
rad15114
14.1%
hot15086
14.1%
cool11658
10.9%
warm11647
10.9%
ht662
 
0.6%
pump662
 
0.6%
base131
 
0.1%
Other values (14)455
 
0.4%

Most occurring characters

ValueCountFrequency (%)
r63341
12.9%
61002
12.4%
o56522
11.5%
a42241
 
8.6%
e33545
 
6.8%
d33364
 
6.8%
t30983
 
6.3%
W26869
 
5.5%
c18313
 
3.7%
i18235
 
3.7%
Other values (26)105932
21.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter322480
65.8%
Uppercase Letter106839
 
21.8%
Space Separator61002
 
12.4%
Dash Punctuation26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r63341
19.6%
o56522
17.5%
a42241
13.1%
e33545
10.4%
d33364
10.3%
t30983
9.6%
c18313
 
5.7%
i18235
 
5.7%
m12309
 
3.8%
l11878
 
3.7%
Other values (9)1749
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
W26869
25.1%
F18196
17.0%
A18147
17.0%
H15748
14.7%
R15114
14.1%
C11658
10.9%
P662
 
0.6%
B262
 
0.2%
E100
 
0.1%
G27
 
< 0.1%
Other values (5)56
 
0.1%
Space Separator
ValueCountFrequency (%)
61002
100.0%
Dash Punctuation
ValueCountFrequency (%)
-26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin429319
87.6%
Common61028
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r63341
14.8%
o56522
13.2%
a42241
9.8%
e33545
 
7.8%
d33364
 
7.8%
t30983
 
7.2%
W26869
 
6.3%
c18313
 
4.3%
i18235
 
4.2%
F18196
 
4.2%
Other values (24)87710
20.4%
Common
ValueCountFrequency (%)
61002
> 99.9%
-26
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII490347
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r63341
12.9%
61002
12.4%
o56522
11.5%
a42241
 
8.6%
e33545
 
6.8%
d33364
 
6.8%
t30983
 
6.3%
W26869
 
5.5%
c18313
 
3.7%
i18235
 
3.7%
Other values (26)105932
21.6%

AC
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
1
35227 
0
10584 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45811
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
135227
76.9%
010584
 
23.1%

Length

2021-07-10T09:23:49.758624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-10T09:23:49.882385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
135227
76.9%
010584
 
23.1%

Most occurring characters

ValueCountFrequency (%)
135227
76.9%
010584
 
23.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number45811
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
135227
76.9%
010584
 
23.1%

Most occurring scripts

ValueCountFrequency (%)
Common45811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
135227
76.9%
010584
 
23.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII45811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135227
76.9%
010584
 
23.1%

YR_RMDL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct97
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1211.445286
Minimum0
Maximum2019
Zeros18129
Zeros (%)39.6%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:49.986517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1999
Q32010
95-th percentile2016
Maximum2019
Range2019
Interquartile range (IQR)2010

Descriptive statistics

Standard deviation980.4313274
Coefficient of variation (CV)0.8093071463
Kurtosis-1.818079808
Mean1211.445286
Median Absolute Deviation (MAD)17
Skewness-0.4261638437
Sum55497520
Variance961245.5877
MonotonicityNot monotonic
2021-07-10T09:23:50.198592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
018129
39.6%
20111656
 
3.6%
20131645
 
3.6%
20141576
 
3.4%
20121549
 
3.4%
20101545
 
3.4%
20151480
 
3.2%
20041446
 
3.2%
20051389
 
3.0%
20061314
 
2.9%
Other values (87)14082
30.7%
ValueCountFrequency (%)
018129
39.6%
18801
 
< 0.1%
19001
 
< 0.1%
19101
 
< 0.1%
19201
 
< 0.1%
19231
 
< 0.1%
19253
 
< 0.1%
19261
 
< 0.1%
19271
 
< 0.1%
19281
 
< 0.1%
ValueCountFrequency (%)
20191
 
< 0.1%
2018309
 
0.7%
20171185
2.6%
20161277
2.8%
20151480
3.2%
20141576
3.4%
20131645
3.6%
20121549
3.4%
20111656
3.6%
20101545
3.4%

EYB
Real number (ℝ≥0)

HIGH CORRELATION

Distinct80
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.091441
Minimum1928
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:50.417866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1928
5-th percentile1950
Q11960
median1967
Q31978
95-th percentile2011
Maximum2018
Range90
Interquartile range (IQR)18

Descriptive statistics

Standard deviation16.9704238
Coefficient of variation (CV)0.008609658307
Kurtosis0.8640949167
Mean1971.091441
Median Absolute Deviation (MAD)7
Skewness1.135924983
Sum90297670
Variance287.9952839
MonotonicityNot monotonic
2021-07-10T09:23:50.625081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19675859
 
12.8%
19644950
 
10.8%
19694361
 
9.5%
19543242
 
7.1%
19573160
 
6.9%
19601811
 
4.0%
19721776
 
3.9%
19781228
 
2.7%
19431010
 
2.2%
1982986
 
2.2%
Other values (70)17428
38.0%
ValueCountFrequency (%)
19281
 
< 0.1%
19321
 
< 0.1%
19363
 
< 0.1%
19402
 
< 0.1%
19431010
2.2%
194420
 
< 0.1%
194521
 
< 0.1%
194628
 
0.1%
1947694
1.5%
194835
 
0.1%
ValueCountFrequency (%)
2018103
 
0.2%
2017440
1.0%
2016233
0.5%
2015575
1.3%
2014330
0.7%
2013318
0.7%
2012251
0.5%
2011433
0.9%
2010450
1.0%
200997
 
0.2%

PRICE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7173
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean628380.4609
Minimum250
Maximum22000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:50.836780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile115464
Q1290000
median507000
Q3800000
95-th percentile1500000
Maximum22000000
Range21999750
Interquartile range (IQR)510000

Descriptive statistics

Standard deviation571595.0969
Coefficient of variation (CV)0.9096321934
Kurtosis104.842387
Mean628380.4609
Median Absolute Deviation (MAD)246000
Skewness5.98541281
Sum2.878673729 × 1010
Variance3.267209548 × 1011
MonotonicityNot monotonic
2021-07-10T09:23:51.053357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000317
 
0.7%
250000245
 
0.5%
550000234
 
0.5%
450000232
 
0.5%
650000228
 
0.5%
300000220
 
0.5%
325000219
 
0.5%
600000219
 
0.5%
320000215
 
0.5%
750000209
 
0.5%
Other values (7163)43473
94.9%
ValueCountFrequency (%)
2502
< 0.1%
48502
< 0.1%
74251
< 0.1%
75001
< 0.1%
104001
< 0.1%
110001
< 0.1%
148001
< 0.1%
150002
< 0.1%
170001
< 0.1%
205001
< 0.1%
ValueCountFrequency (%)
220000001
< 0.1%
180000001
< 0.1%
150000001
< 0.1%
122500001
< 0.1%
119840001
< 0.1%
111111111
< 0.1%
107500001
< 0.1%
90000001
< 0.1%
86000001
< 0.1%
84500002
< 0.1%

SALE_NUM
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.922507695
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:51.239058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile5
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.498678034
Coefficient of variation (CV)0.7795433217
Kurtosis3.179325077
Mean1.922507695
Median Absolute Deviation (MAD)0
Skewness1.752193813
Sum88072
Variance2.246035851
MonotonicityNot monotonic
2021-07-10T09:23:51.398213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
129638
64.7%
35339
 
11.7%
43956
 
8.6%
23450
 
7.5%
51921
 
4.2%
6905
 
2.0%
7348
 
0.8%
8141
 
0.3%
971
 
0.2%
1023
 
0.1%
Other values (4)19
 
< 0.1%
ValueCountFrequency (%)
129638
64.7%
23450
 
7.5%
35339
 
11.7%
43956
 
8.6%
51921
 
4.2%
6905
 
2.0%
7348
 
0.8%
8141
 
0.3%
971
 
0.2%
1023
 
0.1%
ValueCountFrequency (%)
152
 
< 0.1%
131
 
< 0.1%
126
 
< 0.1%
1110
 
< 0.1%
1023
 
0.1%
971
 
0.2%
8141
 
0.3%
7348
 
0.8%
6905
2.0%
51921
4.2%

STYLE
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
2 Story
34840 
3 Story
4577 
2.5 Story Fin
 
3310
1 Story
 
1388
1.5 Story Fin
 
842
Other values (12)
 
854

Length

Max length15
Median length7
Mean length7.63421449
Min length6

Characters and Unicode

Total characters349731
Distinct characters29
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3 Story
2nd row3 Story
3rd row3 Story
4th row4 Story
5th row3 Story

Common Values

ValueCountFrequency (%)
2 Story34840
76.1%
3 Story4577
 
10.0%
2.5 Story Fin3310
 
7.2%
1 Story1388
 
3.0%
1.5 Story Fin842
 
1.8%
2.5 Story Unfin303
 
0.7%
4 Story187
 
0.4%
Split Level132
 
0.3%
Split Foyer88
 
0.2%
3.5 Story Fin71
 
0.2%
Other values (7)73
 
0.2%

Length

2021-07-10T09:23:51.776403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
story45569
47.4%
234840
36.2%
34577
 
4.8%
fin4224
 
4.4%
2.53613
 
3.8%
11388
 
1.4%
1.5885
 
0.9%
unfin353
 
0.4%
split220
 
0.2%
4187
 
0.2%
Other values (7)321
 
0.3%

Most occurring characters

ValueCountFrequency (%)
50366
14.4%
t45803
13.1%
S45789
13.1%
o45657
13.1%
r45657
13.1%
y45657
13.1%
238453
11.0%
n4932
 
1.4%
i4805
 
1.4%
34653
 
1.3%
Other values (19)17959
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter194018
55.5%
Uppercase Letter50616
 
14.5%
Space Separator50366
 
14.4%
Decimal Number50146
 
14.3%
Other Punctuation4577
 
1.3%
Dash Punctuation8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t45803
23.6%
o45657
23.5%
r45657
23.5%
y45657
23.5%
n4932
 
2.5%
i4805
 
2.5%
e380
 
0.2%
l372
 
0.2%
f365
 
0.2%
p220
 
0.1%
Other values (4)170
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
S45789
90.5%
F4312
 
8.5%
U353
 
0.7%
L140
 
0.3%
D12
 
< 0.1%
B8
 
< 0.1%
V2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
238453
76.7%
34653
 
9.3%
54577
 
9.1%
12273
 
4.5%
4190
 
0.4%
Space Separator
ValueCountFrequency (%)
50366
100.0%
Other Punctuation
ValueCountFrequency (%)
.4577
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin244634
69.9%
Common105097
30.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t45803
18.7%
S45789
18.7%
o45657
18.7%
r45657
18.7%
y45657
18.7%
n4932
 
2.0%
i4805
 
2.0%
F4312
 
1.8%
e380
 
0.2%
l372
 
0.2%
Other values (11)1270
 
0.5%
Common
ValueCountFrequency (%)
50366
47.9%
238453
36.6%
34653
 
4.4%
.4577
 
4.4%
54577
 
4.4%
12273
 
2.2%
4190
 
0.2%
-8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII349731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50366
14.4%
t45803
13.1%
S45789
13.1%
o45657
13.1%
r45657
13.1%
y45657
13.1%
238453
11.0%
n4932
 
1.4%
i4805
 
1.4%
34653
 
1.3%
Other values (19)17959
 
5.1%

STRUCT
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Row Inside
19029 
Single
12809 
Semi-Detached
6496 
Row End
5525 
Multi
 
1732
Other values (3)
 
220

Length

Max length13
Median length10
Mean length8.756543188
Min length5

Characters and Unicode

Total characters401146
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRow Inside
2nd rowRow Inside
3rd rowRow Inside
4th rowRow Inside
5th rowRow Inside

Common Values

ValueCountFrequency (%)
Row Inside19029
41.5%
Single12809
28.0%
Semi-Detached6496
 
14.2%
Row End5525
 
12.1%
Multi1732
 
3.8%
Town Inside154
 
0.3%
Town End63
 
0.1%
Default3
 
< 0.1%

Length

2021-07-10T09:23:52.151662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-10T09:23:52.303821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
row24554
34.8%
inside19183
27.2%
single12809
18.1%
semi-detached6496
 
9.2%
end5588
 
7.9%
multi1732
 
2.5%
town217
 
0.3%
default3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e51483
12.8%
i40220
 
10.0%
n37797
 
9.4%
d31267
 
7.8%
o24771
 
6.2%
w24771
 
6.2%
24771
 
6.2%
R24554
 
6.1%
S19305
 
4.8%
I19183
 
4.8%
Other values (15)103024
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter292801
73.0%
Uppercase Letter77078
 
19.2%
Space Separator24771
 
6.2%
Dash Punctuation6496
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e51483
17.6%
i40220
13.7%
n37797
12.9%
d31267
10.7%
o24771
8.5%
w24771
8.5%
s19183
 
6.6%
l14544
 
5.0%
g12809
 
4.4%
t8231
 
2.8%
Other values (6)27725
9.5%
Uppercase Letter
ValueCountFrequency (%)
R24554
31.9%
S19305
25.0%
I19183
24.9%
D6499
 
8.4%
E5588
 
7.2%
M1732
 
2.2%
T217
 
0.3%
Space Separator
ValueCountFrequency (%)
24771
100.0%
Dash Punctuation
ValueCountFrequency (%)
-6496
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin369879
92.2%
Common31267
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e51483
13.9%
i40220
10.9%
n37797
10.2%
d31267
8.5%
o24771
 
6.7%
w24771
 
6.7%
R24554
 
6.6%
S19305
 
5.2%
I19183
 
5.2%
s19183
 
5.2%
Other values (13)77345
20.9%
Common
ValueCountFrequency (%)
24771
79.2%
-6496
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII401146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e51483
12.8%
i40220
 
10.0%
n37797
 
9.4%
d31267
 
7.8%
o24771
 
6.2%
w24771
 
6.2%
24771
 
6.2%
R24554
 
6.1%
S19305
 
4.8%
I19183
 
4.8%
Other values (15)103024
25.7%

GRADE
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Average
14432 
Above Average
13627 
Good Quality
9854 
Very Good
4363 
Excellent
1574 
Other values (7)
1961 

Length

Max length13
Median length12
Mean length10.23112353
Min length7

Characters and Unicode

Total characters468698
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Good
2nd rowVery Good
3rd rowVery Good
4th rowVery Good
5th rowVery Good

Common Values

ValueCountFrequency (%)
Average14432
31.5%
Above Average13627
29.7%
Good Quality9854
21.5%
Very Good4363
 
9.5%
Excellent1574
 
3.4%
Superior1321
 
2.9%
Exceptional-A395
 
0.9%
Exceptional-B145
 
0.3%
Fair Quality42
 
0.1%
Exceptional-C32
 
0.1%
Other values (2)26
 
0.1%

Length

2021-07-10T09:23:52.630834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
average28059
38.1%
good14217
19.3%
above13627
18.5%
quality9898
 
13.4%
very4363
 
5.9%
excellent1574
 
2.1%
superior1321
 
1.8%
exceptional-a395
 
0.5%
exceptional-b145
 
0.2%
fair42
 
0.1%
Other values (3)58
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e79173
16.9%
o43980
9.4%
A42081
9.0%
v41686
8.9%
a38595
 
8.2%
r35106
 
7.5%
g28059
 
6.0%
27888
 
6.0%
y14261
 
3.0%
G14217
 
3.0%
Other values (21)103652
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter365919
78.1%
Uppercase Letter74295
 
15.9%
Space Separator27888
 
6.0%
Dash Punctuation596
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e79173
21.6%
o43980
12.0%
v41686
11.4%
a38595
10.5%
r35106
9.6%
g28059
 
7.7%
y14261
 
3.9%
d14217
 
3.9%
l13642
 
3.7%
b13627
 
3.7%
Other values (8)43573
11.9%
Uppercase Letter
ValueCountFrequency (%)
A42081
56.6%
G14217
 
19.1%
Q9898
 
13.3%
V4363
 
5.9%
E2170
 
2.9%
S1321
 
1.8%
B145
 
0.2%
F42
 
0.1%
C32
 
< 0.1%
D24
 
< 0.1%
Space Separator
ValueCountFrequency (%)
27888
100.0%
Dash Punctuation
ValueCountFrequency (%)
-596
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin440214
93.9%
Common28484
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e79173
18.0%
o43980
10.0%
A42081
9.6%
v41686
9.5%
a38595
8.8%
r35106
 
8.0%
g28059
 
6.4%
y14261
 
3.2%
G14217
 
3.2%
d14217
 
3.2%
Other values (19)88839
20.2%
Common
ValueCountFrequency (%)
27888
97.9%
-596
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII468698
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e79173
16.9%
o43980
9.4%
A42081
9.0%
v41686
8.9%
a38595
 
8.2%
r35106
 
7.5%
g28059
 
6.0%
27888
 
6.0%
y14261
 
3.0%
G14217
 
3.0%
Other values (21)103652
22.1%

CNDTN
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Good
21897 
Average
16771 
Very Good
6112 
Excellent
 
772
Fair
 
227

Length

Max length9
Median length7
Mean length5.84962127
Min length4

Characters and Unicode

Total characters267977
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowVery Good
3rd rowGood
4th rowGood
5th rowAverage

Common Values

ValueCountFrequency (%)
Good21897
47.8%
Average16771
36.6%
Very Good6112
 
13.3%
Excellent772
 
1.7%
Fair227
 
0.5%
Poor32
 
0.1%

Length

2021-07-10T09:23:52.998402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-10T09:23:53.133022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
good28009
53.9%
average16771
32.3%
very6112
 
11.8%
excellent772
 
1.5%
fair227
 
0.4%
poor32
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o56082
20.9%
e41198
15.4%
G28009
10.5%
d28009
10.5%
r23142
8.6%
a16998
 
6.3%
A16771
 
6.3%
v16771
 
6.3%
g16771
 
6.3%
V6112
 
2.3%
Other values (11)18114
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter209942
78.3%
Uppercase Letter51923
 
19.4%
Space Separator6112
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o56082
26.7%
e41198
19.6%
d28009
13.3%
r23142
11.0%
a16998
 
8.1%
v16771
 
8.0%
g16771
 
8.0%
y6112
 
2.9%
l1544
 
0.7%
x772
 
0.4%
Other values (4)2543
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
G28009
53.9%
A16771
32.3%
V6112
 
11.8%
E772
 
1.5%
F227
 
0.4%
P32
 
0.1%
Space Separator
ValueCountFrequency (%)
6112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin261865
97.7%
Common6112
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o56082
21.4%
e41198
15.7%
G28009
10.7%
d28009
10.7%
r23142
8.8%
a16998
 
6.5%
A16771
 
6.4%
v16771
 
6.4%
g16771
 
6.4%
V6112
 
2.3%
Other values (10)12002
 
4.6%
Common
ValueCountFrequency (%)
6112
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII267977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o56082
20.9%
e41198
15.4%
G28009
10.5%
d28009
10.5%
r23142
8.6%
a16998
 
6.3%
A16771
 
6.3%
v16771
 
6.3%
g16771
 
6.3%
V6112
 
2.3%
Other values (11)18114
 
6.8%

EXTWALL
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Common Brick
34433 
Brick/Siding
 
2915
Vinyl Siding
 
2317
Wood Siding
 
1767
Stucco
 
1292
Other values (18)
 
3087

Length

Max length14
Median length12
Mean length11.648687
Min length5

Characters and Unicode

Total characters533638
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCommon Brick
2nd rowCommon Brick
3rd rowCommon Brick
4th rowCommon Brick
5th rowCommon Brick

Common Values

ValueCountFrequency (%)
Common Brick34433
75.2%
Brick/Siding2915
 
6.4%
Vinyl Siding2317
 
5.1%
Wood Siding1767
 
3.9%
Stucco1292
 
2.8%
Brick Veneer483
 
1.1%
Shingle378
 
0.8%
Face Brick369
 
0.8%
Brick/Stucco343
 
0.7%
Aluminum324
 
0.7%
Other values (13)1190
 
2.6%

Length

2021-07-10T09:23:53.459117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
brick35285
41.3%
common34433
40.3%
siding4103
 
4.8%
brick/siding2915
 
3.4%
vinyl2317
 
2.7%
wood1767
 
2.1%
stucco1312
 
1.5%
veneer603
 
0.7%
stone416
 
0.5%
shingle378
 
0.4%
Other values (13)1837
 
2.2%

Most occurring characters

ValueCountFrequency (%)
o75376
14.1%
m69514
13.0%
i56263
10.5%
n46339
8.7%
c42897
8.0%
r39622
7.4%
39555
7.4%
B38905
7.3%
k38905
7.3%
C34490
6.5%
Other values (22)51772
9.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter400965
75.1%
Uppercase Letter89242
 
16.7%
Space Separator39555
 
7.4%
Other Punctuation3876
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o75376
18.8%
m69514
17.3%
i56263
14.0%
n46339
11.6%
c42897
10.7%
r39622
9.9%
k38905
9.7%
d9066
 
2.3%
g7571
 
1.9%
e3730
 
0.9%
Other values (9)11682
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
B38905
43.6%
C34490
38.6%
S10388
 
11.6%
V2920
 
3.3%
W1767
 
2.0%
F369
 
0.4%
A325
 
0.4%
H52
 
0.1%
M19
 
< 0.1%
D6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
39555
100.0%
Other Punctuation
ValueCountFrequency (%)
/3876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin490207
91.9%
Common43431
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o75376
15.4%
m69514
14.2%
i56263
11.5%
n46339
9.5%
c42897
8.8%
r39622
8.1%
B38905
7.9%
k38905
7.9%
C34490
7.0%
S10388
 
2.1%
Other values (20)37508
7.7%
Common
ValueCountFrequency (%)
39555
91.1%
/3876
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII533638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o75376
14.1%
m69514
13.0%
i56263
10.5%
n46339
8.7%
c42897
8.0%
r39622
7.4%
39555
7.4%
B38905
7.3%
k38905
7.3%
C34490
6.5%
Other values (22)51772
9.7%

ROOF
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Built Up
13621 
Metal- Sms
13013 
Comp Shingle
12706 
Slate
4673 
Neopren
 
795
Other values (11)
 
1003

Length

Max length14
Median length10
Mean length9.347231014
Min length5

Characters and Unicode

Total characters428206
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowMetal- Sms
2nd rowBuilt Up
3rd rowBuilt Up
4th rowBuilt Up
5th rowMetal- Sms

Common Values

ValueCountFrequency (%)
Built Up13621
29.7%
Metal- Sms13013
28.4%
Comp Shingle12706
27.7%
Slate4673
 
10.2%
Neopren795
 
1.7%
Shake303
 
0.7%
Clay Tile247
 
0.5%
Shingle194
 
0.4%
Metal- Pre104
 
0.2%
Typical70
 
0.2%
Other values (6)85
 
0.2%

Length

2021-07-10T09:23:53.829983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
built13621
15.9%
up13621
15.9%
metal13136
15.3%
sms13013
15.2%
shingle12900
15.1%
comp12706
14.8%
slate4673
 
5.5%
neopren795
 
0.9%
shake303
 
0.4%
tile251
 
0.3%
Other values (11)567
 
0.7%

Most occurring characters

ValueCountFrequency (%)
l44898
 
10.5%
39775
 
9.3%
e32969
 
7.7%
t31493
 
7.4%
S30892
 
7.2%
p27267
 
6.4%
i26954
 
6.3%
m25775
 
6.0%
a18431
 
4.3%
n13756
 
3.2%
Other values (22)135996
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter289703
67.7%
Uppercase Letter85589
 
20.0%
Space Separator39775
 
9.3%
Dash Punctuation13139
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l44898
15.5%
e32969
11.4%
t31493
10.9%
p27267
9.4%
i26954
9.3%
m25775
8.9%
a18431
6.4%
n13756
 
4.7%
o13740
 
4.7%
u13621
 
4.7%
Other values (9)40799
14.1%
Uppercase Letter
ValueCountFrequency (%)
S30892
36.1%
B13621
15.9%
U13621
15.9%
M13136
15.3%
C13033
15.2%
N795
 
0.9%
T321
 
0.4%
P106
 
0.1%
R56
 
0.1%
W5
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-13139
100.0%
Space Separator
ValueCountFrequency (%)
39775
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin375292
87.6%
Common52914
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l44898
12.0%
e32969
 
8.8%
t31493
 
8.4%
S30892
 
8.2%
p27267
 
7.3%
i26954
 
7.2%
m25775
 
6.9%
a18431
 
4.9%
n13756
 
3.7%
o13740
 
3.7%
Other values (20)109117
29.1%
Common
ValueCountFrequency (%)
39775
75.2%
-13139
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII428206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l44898
 
10.5%
39775
 
9.3%
e32969
 
7.7%
t31493
 
7.4%
S30892
 
7.2%
p27267
 
6.4%
i26954
 
6.3%
m25775
 
6.0%
a18431
 
4.3%
n13756
 
3.2%
Other values (22)135996
31.8%

INTWALL
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Hardwood
35309 
Hardwood/Carp
6031 
Wood Floor
 
2936
Carpet
 
1404
Lt Concrete
 
37
Other values (7)
 
94

Length

Max length13
Median length8
Mean length8.730588723
Min length6

Characters and Unicode

Total characters399957
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHardwood
2nd rowHardwood
3rd rowHardwood
4th rowHardwood
5th rowHardwood

Common Values

ValueCountFrequency (%)
Hardwood35309
77.1%
Hardwood/Carp6031
 
13.2%
Wood Floor2936
 
6.4%
Carpet1404
 
3.1%
Lt Concrete37
 
0.1%
Ceramic Tile36
 
0.1%
Default28
 
0.1%
Parquet9
 
< 0.1%
Vinyl Comp7
 
< 0.1%
Resiliant6
 
< 0.1%
Other values (2)8
 
< 0.1%

Length

2021-07-10T09:23:54.199501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hardwood35309
72.3%
hardwood/carp6031
 
12.4%
floor2936
 
6.0%
wood2936
 
6.0%
carpet1404
 
2.9%
lt37
 
0.1%
concrete37
 
0.1%
tile36
 
0.1%
ceramic36
 
0.1%
default28
 
0.1%
Other values (6)42
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o94471
23.6%
d85616
21.4%
r51799
13.0%
a48857
12.2%
H41340
10.3%
w41340
10.3%
C7515
 
1.9%
p7442
 
1.9%
/6031
 
1.5%
3021
 
0.8%
Other values (23)12525
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter336042
84.0%
Uppercase Letter54863
 
13.7%
Other Punctuation6031
 
1.5%
Space Separator3021
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o94471
28.1%
d85616
25.5%
r51799
15.4%
a48857
14.5%
w41340
12.3%
p7442
 
2.2%
l3018
 
0.9%
e1606
 
0.5%
t1526
 
0.5%
i96
 
< 0.1%
Other values (10)271
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
H41340
75.4%
C7515
 
13.7%
W2936
 
5.4%
F2936
 
5.4%
T39
 
0.1%
L37
 
0.1%
D28
 
0.1%
V12
 
< 0.1%
P9
 
< 0.1%
R6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
3021
100.0%
Other Punctuation
ValueCountFrequency (%)
/6031
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin390905
97.7%
Common9052
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o94471
24.2%
d85616
21.9%
r51799
13.3%
a48857
12.5%
H41340
10.6%
w41340
10.6%
C7515
 
1.9%
p7442
 
1.9%
l3018
 
0.8%
W2936
 
0.8%
Other values (21)6571
 
1.7%
Common
ValueCountFrequency (%)
/6031
66.6%
3021
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII399957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o94471
23.6%
d85616
21.4%
r51799
13.0%
a48857
12.2%
H41340
10.3%
w41340
10.3%
C7515
 
1.9%
p7442
 
1.9%
/6031
 
1.5%
3021
 
0.8%
Other values (23)12525
 
3.1%

KITCHENS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.227936522
Minimum0
Maximum6
Zeros24
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:54.365455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5899153747
Coefficient of variation (CV)0.4804119466
Kurtosis10.77401125
Mean1.227936522
Median Absolute Deviation (MAD)0
Skewness3.14934249
Sum56253
Variance0.3480001493
MonotonicityNot monotonic
2021-07-10T09:23:54.519015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
138066
83.1%
26134
 
13.4%
41141
 
2.5%
3439
 
1.0%
024
 
0.1%
54
 
< 0.1%
63
 
< 0.1%
ValueCountFrequency (%)
024
 
0.1%
138066
83.1%
26134
 
13.4%
3439
 
1.0%
41141
 
2.5%
54
 
< 0.1%
63
 
< 0.1%
ValueCountFrequency (%)
63
 
< 0.1%
54
 
< 0.1%
41141
 
2.5%
3439
 
1.0%
26134
 
13.4%
138066
83.1%
024
 
0.1%

FIREPLACES
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6825871516
Minimum0
Maximum13
Zeros24362
Zeros (%)53.2%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:54.679334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9257232524
Coefficient of variation (CV)1.356197887
Kurtosis8.175808408
Mean0.6825871516
Median Absolute Deviation (MAD)0
Skewness2.048501274
Sum31270
Variance0.8569635401
MonotonicityNot monotonic
2021-07-10T09:23:54.852527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
024362
53.2%
114554
31.8%
25073
 
11.1%
31162
 
2.5%
4405
 
0.9%
5145
 
0.3%
673
 
0.2%
722
 
< 0.1%
85
 
< 0.1%
93
 
< 0.1%
Other values (4)7
 
< 0.1%
ValueCountFrequency (%)
024362
53.2%
114554
31.8%
25073
 
11.1%
31162
 
2.5%
4405
 
0.9%
5145
 
0.3%
673
 
0.2%
722
 
< 0.1%
85
 
< 0.1%
93
 
< 0.1%
ValueCountFrequency (%)
132
 
< 0.1%
121
 
< 0.1%
112
 
< 0.1%
102
 
< 0.1%
93
 
< 0.1%
85
 
< 0.1%
722
 
< 0.1%
673
 
0.2%
5145
 
0.3%
4405
0.9%

LANDAREA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7834
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3154.642182
Minimum0
Maximum155905
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:55.043567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile895
Q11501
median2178
Q34000
95-th percentile7985
Maximum155905
Range155905
Interquartile range (IQR)2499

Descriptive statistics

Standard deviation2975.201257
Coefficient of variation (CV)0.9431184537
Kurtosis216.2697942
Mean3154.642182
Median Absolute Deviation (MAD)880
Skewness7.753024619
Sum144517313
Variance8851822.518
MonotonicityNot monotonic
2021-07-10T09:23:55.270036image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800473
 
1.0%
2000459
 
1.0%
4000347
 
0.8%
1600339
 
0.7%
5000331
 
0.7%
1700259
 
0.6%
1440257
 
0.6%
1500244
 
0.5%
1620215
 
0.5%
2500214
 
0.5%
Other values (7824)42673
93.2%
ValueCountFrequency (%)
01
 
< 0.1%
2161
 
< 0.1%
2551
 
< 0.1%
2881
 
< 0.1%
2941
 
< 0.1%
3273
< 0.1%
3311
 
< 0.1%
3502
< 0.1%
3531
 
< 0.1%
3572
< 0.1%
ValueCountFrequency (%)
1559051
< 0.1%
953701
< 0.1%
737711
< 0.1%
677271
< 0.1%
642051
< 0.1%
614201
< 0.1%
578311
< 0.1%
543351
< 0.1%
530341
< 0.1%
463041
< 0.1%

ZIPCODE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20011.47587
Minimum20001
Maximum20052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:55.889085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum20001
5-th percentile20001
Q120003
median20011
Q320017
95-th percentile20020
Maximum20052
Range51
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.600664145
Coefficient of variation (CV)0.0003798152717
Kurtosis0.2993417555
Mean20011.47587
Median Absolute Deviation (MAD)7
Skewness0.5642634287
Sum916745721
Variance57.77009545
MonotonicityNot monotonic
2021-07-10T09:23:56.073331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
200026241
13.6%
200115934
13.0%
200194431
9.7%
200033242
 
7.1%
200163176
 
6.9%
200073116
 
6.8%
200202679
 
5.8%
200012438
 
5.3%
200152292
 
5.0%
200182070
 
4.5%
Other values (11)10192
22.2%
ValueCountFrequency (%)
200012438
 
5.3%
200026241
13.6%
200033242
7.1%
20005104
 
0.2%
200073116
6.8%
200081568
 
3.4%
200091599
 
3.5%
200101800
 
3.9%
200115934
13.0%
200121372
 
3.0%
ValueCountFrequency (%)
200526
 
< 0.1%
20037166
 
0.4%
2003667
 
0.1%
200321427
 
3.1%
20024272
 
0.6%
200202679
5.8%
200194431
9.7%
200182070
4.5%
200171811
4.0%
200163176
6.9%

LATITUDE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45386
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.91697554
Minimum38.81995335
Maximum38.9954352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size358.0 KiB
2021-07-10T09:23:56.228251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum38.81995335
5-th percentile38.85881964
Q138.89372785
median38.91720139
Q338.94337687
95-th percentile38.96777483
Maximum38.9954352
Range0.17548185
Interquartile range (IQR)0.049649025

Descriptive statistics

Standard deviation0.03338690058
Coefficient of variation (CV)0.0008579007006
Kurtosis-0.2883711537
Mean38.91697554
Median Absolute Deviation (MAD)0.02465457
Skewness-0.2789265642
Sum1782825.567
Variance0.001114685131
MonotonicityNot monotonic
2021-07-10T09:23:56.469773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.8996960940
 
0.1%
38.9605065110
 
< 0.1%
38.902747319
 
< 0.1%
38.927028759
 
< 0.1%
38.926883998
 
< 0.1%
38.926506817
 
< 0.1%
38.923354155
 
< 0.1%
38.926698515
 
< 0.1%
38.913529665
 
< 0.1%
38.896298494
 
< 0.1%
Other values (45376)45709
99.8%
ValueCountFrequency (%)
38.819953351
< 0.1%
38.820060291
< 0.1%
38.820218671
< 0.1%
38.8202721
< 0.1%
38.820383621
< 0.1%
38.820755481
< 0.1%
38.820768751
< 0.1%
38.820773661
< 0.1%
38.820823491
< 0.1%
38.8210411
< 0.1%
ValueCountFrequency (%)
38.99543521
< 0.1%
38.994894231
< 0.1%
38.994797291
< 0.1%
38.994751161
< 0.1%
38.994709761
< 0.1%
38.994575141
< 0.1%
38.994413511
< 0.1%
38.994205581
< 0.1%
38.994143661
< 0.1%
38.994128421
< 0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45517
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-77.01259515
Minimum-77.11390873
Maximum-76.9097583
Zeros0
Zeros (%)0.0%
Negative45811
Negative (%)100.0%
Memory size358.0 KiB
2021-07-10T09:23:56.697306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-77.11390873
5-th percentile-77.08743656
Q1-77.04017364
median-77.00959268
Q3-76.98480747
95-th percentile-76.93557863
Maximum-76.9097583
Range0.20415043
Interquartile range (IQR)0.055366175

Descriptive statistics

Standard deviation0.04359272504
Coefficient of variation (CV)-0.000566046696
Kurtosis-0.5107156491
Mean-77.01259515
Median Absolute Deviation (MAD)0.0264139
Skewness-0.08265261743
Sum-3528023.997
Variance0.001900325676
MonotonicityNot monotonic
2021-07-10T09:23:56.916910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-76.9515085141
 
0.1%
-77.0736647310
 
< 0.1%
-76.9509318
 
< 0.1%
-76.953984725
 
< 0.1%
-77.000797695
 
< 0.1%
-77.000793834
 
< 0.1%
-77.066893544
 
< 0.1%
-76.999291563
 
< 0.1%
-76.999341953
 
< 0.1%
-76.996399673
 
< 0.1%
Other values (45507)45725
99.8%
ValueCountFrequency (%)
-77.113908731
< 0.1%
-77.11380971
< 0.1%
-77.113774211
< 0.1%
-77.1133891
< 0.1%
-77.113320661
< 0.1%
-77.113149861
< 0.1%
-77.113048781
< 0.1%
-77.112709641
< 0.1%
-77.112635721
< 0.1%
-77.112526891
< 0.1%
ValueCountFrequency (%)
-76.90975831
< 0.1%
-76.909842661
< 0.1%
-76.90996991
< 0.1%
-76.909983461
< 0.1%
-76.910125791
< 0.1%
-76.91024371
< 0.1%
-76.91027891
< 0.1%
-76.91033391
< 0.1%
-76.91044331
< 0.1%
-76.910545851
< 0.1%

ASSESSMENT_NBHD
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Old City 1
6110 
Petworth
 
2402
Chevy Chase
 
2323
Old City 2
 
2269
Deanwood
 
2155
Other values (50)
30552 

Length

Max length28
Median length10
Mean length11.23313178
Min length4

Characters and Unicode

Total characters514601
Distinct characters49
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOld City 2
2nd rowOld City 2
3rd rowOld City 2
4th rowOld City 2
5th rowOld City 2

Common Values

ValueCountFrequency (%)
Old City 16110
 
13.3%
Petworth2402
 
5.2%
Chevy Chase2323
 
5.1%
Old City 22269
 
5.0%
Deanwood2155
 
4.7%
Columbia Heights2123
 
4.6%
Brookland2052
 
4.5%
Capitol Hill1483
 
3.2%
Brightwood1437
 
3.1%
Congress Heights1324
 
2.9%
Other values (45)22133
48.3%

Length

2021-07-10T09:23:57.360724image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
old8379
 
9.9%
city8379
 
9.9%
heights6793
 
8.0%
16198
 
7.3%
park4749
 
5.6%
petworth2402
 
2.8%
chase2323
 
2.7%
chevy2323
 
2.7%
22269
 
2.7%
deanwood2155
 
2.5%
Other values (64)39068
45.9%

Most occurring characters

ValueCountFrequency (%)
39227
 
7.6%
t38525
 
7.5%
e37125
 
7.2%
i36941
 
7.2%
o33862
 
6.6%
l31228
 
6.1%
a28703
 
5.6%
r27663
 
5.4%
d23379
 
4.5%
s20374
 
4.0%
Other values (39)197574
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter388673
75.5%
Uppercase Letter75781
 
14.7%
Space Separator39227
 
7.6%
Decimal Number10047
 
2.0%
Other Punctuation785
 
0.2%
Dash Punctuation88
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t38525
9.9%
e37125
9.6%
i36941
9.5%
o33862
 
8.7%
l31228
 
8.0%
a28703
 
7.4%
r27663
 
7.1%
d23379
 
6.0%
s20374
 
5.2%
n20057
 
5.2%
Other values (13)90816
23.4%
Uppercase Letter
ValueCountFrequency (%)
C19914
26.3%
H9532
12.6%
P9004
11.9%
O8582
11.3%
B4812
 
6.3%
D3269
 
4.3%
F2417
 
3.2%
R2138
 
2.8%
G1974
 
2.6%
M1845
 
2.4%
Other values (10)12294
16.2%
Decimal Number
ValueCountFrequency (%)
16988
69.6%
22269
 
22.6%
6790
 
7.9%
Space Separator
ValueCountFrequency (%)
39227
100.0%
Dash Punctuation
ValueCountFrequency (%)
-88
100.0%
Other Punctuation
ValueCountFrequency (%)
.785
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin464454
90.3%
Common50147
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t38525
 
8.3%
e37125
 
8.0%
i36941
 
8.0%
o33862
 
7.3%
l31228
 
6.7%
a28703
 
6.2%
r27663
 
6.0%
d23379
 
5.0%
s20374
 
4.4%
n20057
 
4.3%
Other values (33)166597
35.9%
Common
ValueCountFrequency (%)
39227
78.2%
16988
 
13.9%
22269
 
4.5%
6790
 
1.6%
.785
 
1.6%
-88
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII514601
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39227
 
7.6%
t38525
 
7.5%
e37125
 
7.2%
i36941
 
7.2%
o33862
 
6.6%
l31228
 
6.1%
a28703
 
5.6%
r27663
 
5.4%
d23379
 
4.5%
s20374
 
4.0%
Other values (39)197574
38.4%

WARD
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
Ward 4
8331 
Ward 6
7945 
Ward 5
7338 
Ward 3
6925 
Ward 7
5616 
Other values (3)
9656 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters274866
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWard 2
2nd rowWard 2
3rd rowWard 2
4th rowWard 2
5th rowWard 2

Common Values

ValueCountFrequency (%)
Ward 48331
18.2%
Ward 67945
17.3%
Ward 57338
16.0%
Ward 36925
15.1%
Ward 75616
12.3%
Ward 13550
7.7%
Ward 83228
 
7.0%
Ward 22878
 
6.3%

Length

2021-07-10T09:23:57.701628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-10T09:23:57.836826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
ward45811
50.0%
48331
 
9.1%
67945
 
8.7%
57338
 
8.0%
36925
 
7.6%
75616
 
6.1%
13550
 
3.9%
83228
 
3.5%
22878
 
3.1%

Most occurring characters

ValueCountFrequency (%)
W45811
16.7%
a45811
16.7%
r45811
16.7%
d45811
16.7%
45811
16.7%
48331
 
3.0%
67945
 
2.9%
57338
 
2.7%
36925
 
2.5%
75616
 
2.0%
Other values (3)9656
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter137433
50.0%
Uppercase Letter45811
 
16.7%
Space Separator45811
 
16.7%
Decimal Number45811
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
48331
18.2%
67945
17.3%
57338
16.0%
36925
15.1%
75616
12.3%
13550
7.7%
83228
 
7.0%
22878
 
6.3%
Lowercase Letter
ValueCountFrequency (%)
a45811
33.3%
r45811
33.3%
d45811
33.3%
Uppercase Letter
ValueCountFrequency (%)
W45811
100.0%
Space Separator
ValueCountFrequency (%)
45811
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin183244
66.7%
Common91622
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
45811
50.0%
48331
 
9.1%
67945
 
8.7%
57338
 
8.0%
36925
 
7.6%
75616
 
6.1%
13550
 
3.9%
83228
 
3.5%
22878
 
3.1%
Latin
ValueCountFrequency (%)
W45811
25.0%
a45811
25.0%
r45811
25.0%
d45811
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII274866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W45811
16.7%
a45811
16.7%
r45811
16.7%
d45811
16.7%
45811
16.7%
48331
 
3.0%
67945
 
2.9%
57338
 
2.7%
36925
 
2.5%
75616
 
2.0%
Other values (3)9656
 
3.5%

QUADRANT
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size358.0 KiB
NW
22562 
NE
13769 
SE
8940 
SW
 
540

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters91622
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowNW
3rd rowNW
4th rowNW
5th rowNW

Common Values

ValueCountFrequency (%)
NW22562
49.3%
NE13769
30.1%
SE8940
 
19.5%
SW540
 
1.2%

Length

2021-07-10T09:23:58.120579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-10T09:23:58.247603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
nw22562
49.3%
ne13769
30.1%
se8940
 
19.5%
sw540
 
1.2%

Most occurring characters

ValueCountFrequency (%)
N36331
39.7%
W23102
25.2%
E22709
24.8%
S9480
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter91622
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N36331
39.7%
W23102
25.2%
E22709
24.8%
S9480
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin91622
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N36331
39.7%
W23102
25.2%
E22709
24.8%
S9480
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII91622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N36331
39.7%
W23102
25.2%
E22709
24.8%
S9480
 
10.3%

Interactions

2021-07-10T09:23:21.108242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:21.253721image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:21.384308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:21.520625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:21.663435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:21.794296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:21.925235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:22.059596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:22.193112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:22.327628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:22.470964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:22.606624image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:22.748658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:22.898266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:23.034635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:23.176061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:23.335616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:23.479230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:23.610911image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:23.744235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:23.880862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:24.214917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:24.350878image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:24.496101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:24.632891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:24.777046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:24.925040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:25.065490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:25.206292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:25.354246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:25.502730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:25.642570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:25.789729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:25.937432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:26.078127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:26.220243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:26.371597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:26.514366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:26.664099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:26.817571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:26.970420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:27.125635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:27.282449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:27.446031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:27.597345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:27.749110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:27.904466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:28.059936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:28.208746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:28.362879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:28.508626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:28.661401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:28.817974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:28.948852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:29.079627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:29.224187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:29.372760image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:29.718077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:29.854852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:29.992823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:30.131263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:30.263166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:30.402344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:30.533306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:30.672027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:30.815148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:30.953086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:31.094282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:31.228698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:31.367468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:31.495477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:31.623845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:31.754465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:31.887156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:32.027498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:32.172722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:32.312871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:32.453720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:32.596354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:32.733627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:32.870183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.009782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.155319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.295216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.429033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.565526image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.702657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.839225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:33.986068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:34.129847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:34.304770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:34.480283image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:34.626854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:34.772586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:34.919312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:35.073371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:35.205203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:35.340872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:35.475362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:35.611112image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:35.749433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:35.895947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:36.041462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:36.447630image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:36.595262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:36.730894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:36.866918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.013614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.160656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.293256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.426197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.564592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.701854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.839217image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:37.985718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:38.124261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:38.271567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:38.420655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:38.568323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:38.714058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:38.864388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:39.019437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:39.165533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:39.310339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:39.456946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:39.603762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:39.750696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:39.908740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:40.059584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:40.217300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:40.378228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:40.516183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:40.653035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:40.790999image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:40.937701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:41.073564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:41.210731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:41.348332image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:41.487613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:41.627574image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:41.773683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:41.911261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:42.057025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:42.206128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:42.353748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:42.500455image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:42.648932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:42.803863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:42.946337image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:43.098488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:43.246682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:43.395050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:43.542879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:43.698784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:43.844963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:43.999418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:44.161575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:44.317472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:44.801626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:44.956800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:45.122646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:45.293491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:45.458165image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:45.610153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:45.763577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:45.926545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:46.087333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:46.240532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-10T09:23:46.401778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-10T09:23:58.360645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-10T09:23:58.584897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-10T09:23:58.807909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-10T09:23:59.059930image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-10T09:23:59.324769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-10T09:23:46.766543image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-10T09:23:47.580301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0BATHRMHF_BATHRMHEATACYR_RMDLEYBPRICESALE_NUMSTYLESTRUCTGRADECNDTNEXTWALLROOFINTWALLKITCHENSFIREPLACESLANDAREAZIPCODELATITUDELONGITUDEASSESSMENT_NBHDWARDQUADRANT
0040Warm Cool11988.019721095000.013 StoryRow InsideVery GoodGoodCommon BrickMetal- SmsHardwood2.05168020009.038.914680-77.040832Old City 2Ward 2NW
1231Hot Water Rad12009.019842100000.033 StoryRow InsideVery GoodVery GoodCommon BrickBuilt UpHardwood2.04168020009.038.914684-77.040678Old City 2Ward 2NW
2331Hot Water Rad12003.019841602000.013 StoryRow InsideVery GoodGoodCommon BrickBuilt UpHardwood2.03168020009.038.914683-77.040629Old City 2Ward 2NW
3532Hot Water Rad10.019721950000.014 StoryRow InsideVery GoodGoodCommon BrickBuilt UpHardwood1.04219620009.038.914331-77.039715Old City 2Ward 2NW
4731Hot Water Rad12011.019721050000.013 StoryRow InsideVery GoodAverageCommon BrickMetal- SmsHardwood2.01162720009.038.915408-77.040129Old City 2Ward 2NW
5831Warm Cool12008.019671430000.042 StoryRow InsideAbove AverageVery GoodCommon BrickBuilt UpHardwood2.01142420009.038.915017-77.039903Old City 2Ward 2NW
61431Warm Cool12000.019671325000.012 StoryRow InsideAbove AverageVery GoodStuccoMetal- SmsHardwood2.01181520009.038.915038-77.039716Old City 2Ward 2NW
71631Warm Cool12006.019671240000.012 StoryRow InsideAbove AverageVery GoodCommon BrickMetal- SmsHardwood1.00142420009.038.915018-77.039844Old City 2Ward 2NW
81931Hot Water Rad12013.01969592250.012 StoryRow InsideGood QualityGoodCommon BrickBuilt UpHardwood2.01142420009.038.915019-77.040138Old City 2Ward 2NW
92210Forced Air12010.01967907400.012 StorySemi-DetachedAbove AverageGoodCommon BrickBuilt UpHardwood1.00209020009.038.911368-77.033925Old City 2Ward 2NW

Last rows

Unnamed: 0BATHRMHF_BATHRMHEATACYR_RMDLEYBPRICESALE_NUMSTYLESTRUCTGRADECNDTNEXTWALLROOFINTWALLKITCHENSFIREPLACESLANDAREAZIPCODELATITUDELONGITUDEASSESSMENT_NBHDWARDQUADRANT
4580110665621Hot Water Rad12007.01964227000.062 StoryRow InsideAverageGoodCommon BrickBuilt UpHardwood1.00173320032.038.848216-76.997142Congress HeightsWard 8SE
4580210665721Hot Water Rad12008.01957140496.012 StoryRow InsideAverageGoodCommon BrickMetal- SmsHardwood1.01173420032.038.848213-76.997192Congress HeightsWard 8SE
4580310666211Warm Cool10.01976120000.013 StoryRow InsideAverageAverageWood SidingBuilt UpWood Floor1.00279920032.038.825131-76.997396Congress HeightsWard 8SE
4580410666311Warm Cool10.01976109256.013 StoryRow InsideAverageAverageCommon BrickBuilt UpWood Floor1.00257920032.038.825080-76.997430Congress HeightsWard 8SE
4580510666411Warm Cool12013.01988230000.013 StoryRow InsideAverageGoodCommon BrickBuilt UpWood Floor1.00235920032.038.825026-76.997449Congress HeightsWard 8SE
4580610666611Warm Cool12013.01988215000.013 StoryRow InsideAverageGoodCommon BrickBuilt UpWood Floor1.00191920032.038.824922-76.997489Congress HeightsWard 8SE
4580710666821Forced Air10.01988205000.013 StoryRow InsideAverageAverageCommon BrickBuilt UpWood Floor1.00251320032.038.824805-76.997597Congress HeightsWard 8SE
4580810667230Forced Air00.01963100000.012 StoryMultiAverageAverageCommon BrickComp ShingleHardwood3.00437420032.038.820755-77.007009Congress HeightsWard 8SW
4580910667330Forced Air02002.01988103000.012 StoryMultiAverageGoodCommon BrickComp ShingleHardwood3.00452320032.038.820823-77.007013Congress HeightsWard 8SW
4581010668720Forced Air00.0196295000.012 StoryMultiAverageAverageCommon BrickComp ShingleCarpet2.00583720032.038.821855-77.005828Congress HeightsWard 8SW